Exploring Data with R

.title[
# Exploring Data with R
]
.subtitle[
## Data Analysis Using R
]
.author[
### Dr. Arun Mitra
]
.institute[
### AMCHSS, SCTIMST
]

---

### Before I forget..

---

## R is Case Sensitive.

---

## Which is better?

- Matter of personal choice... But...

# Outline of the talk

- What is EDA?

- Generate questions about your data.

- Search for answers by visualising, transforming your data.

- Live Demo Session

---

# What is EDA?

### Exploratory Data Analysis

- **EDA** is a state of mind.

- **EDA** is exploring your ideas.

- **EDA** has no strict rules.

- **EDA** helps understand your data.

- **EDA** is an iterative cycle.

- **EDA** is a creative process.

]

---
class: middle

# What is EDA?

It  is  mostly  a  **philosophy**  of  data analysis where the researcher examines the data without any pre-conceived ideas  in  order  to  discover what  the  data  can  tell  him  or  her  about the phenomena  being  studied.

.left[*"detective work – numerical detective work – or counting detective work – or graphical detective  work"*]

---

# Questions

The easiest way to do **EDA** is to use questions as tools to guide your investigation. **EDA** is an important part of any data analysis, even if the questions are known already.

.left[*"Far better an approximate answer to the right question, which is often vague, than an exact answer to the wrong question, which can always be made precise."*]

---
class: middle

# Asking the right questions

Key to asking **_quality_** questions is to generate a large **_quantity_** of questions.

It is difficult to ask revealing questions at the start of the analysis.

But, each new question will expose a new aspect and increase your chance of making a discovery.

### Questions to ask:

- What type of variation occurs within your variables?

- What type of covariation occurs between your variables?

- Whether your data meets your expectations or not.

- Whether the quality of your data is robust or not.

---
# The process of EDA

.center[![:scale 90%](data:image/png;base64,#data_exploration1.png)]
1. Import
2. Tidy
3. Explore
  - Transform
  - Visualize
  - Transform
  - Visualize
  - Transform 
  - Visualise ...
  - ... 
  
**It is an iterative process**
---

# What did we cover yesterday?

### Concepts

- Health Data Science
- Reproducible Research
- Tidy Data

### How to Visualize Data
- `ggplot2`

### How to Import Data
- `readr`
- `readxl`
- `here::here()`

---

# What will be covered today?

### Preparing Tidy Data
- Data Cleaning
- Data Wrangling

### Data Exploration
- Data Transformation
- Data Visualization

### Statistical Analysis
### Summary Tables

---

# Before we get started

### To recap what we learnt in the previous sessions..

We now know to work within the R Project environment.

`here::here()` makes it easy for us to manage file paths.

You can quickly have a look at your data using the `View()` and `glimpse()` functions.

Most of the tidy data is read as `tibble` which is a workhorse of `tidyverse`.

---

# Getting Started with the Data Exploration Pipeline

## Step Zero: Get your documents and data in place.

- Today we will be working in a RMarkdown Document (`.Rmd`).

- RMarkdown interweaves prose with code.

- Please download the `.Rmd` file provided to you via email and place it in the working directory.

- We will work from within yesterday's project.

-  Yes, `here::here()`!

---

# Today, we will be introducing you to three new packages:

1. `dplyr`

2. `skimr`

3. `DataExplorer`

---

## `dplyr` Package

.left[
The `dplyr` is a powerful R-package to manipulate, clean and summarize unstructured data. In short, it makes data exploration and data manipulation easy and fast in R. 
]

]
---

# Verbs of the `dplyr`

There are many verbs in `dplyr` that are useful.

---
# Syntax of the `dplyr` verbs

![](data:image/png;base64,#dplyr-verb.png)

---

# Getting used to the pipe ` %>% `

### The pipe operator in `dplyr`
![:scale 90%](data:image/png;base64,#dplyr-pipe.png)
The pipe `%>%` means **THEN**...

The pipe is an operator in R that allows you to chain together functions in `dplyr`.
---

## Lets load some sample data

```r
tb <- read_csv(here("who_tubercolosis_data.csv"))

glimpse(tb)
```

```
## Rows: 3,850
## Columns: 18
## $ country                <chr> "Afghanistan", "Afghanistan", "Afghanistan", "A…
## $ who_region             <chr> "EMR", "EMR", "EMR", "EMR", "EMR", "EMR", "EMR"…
## $ year                   <dbl> 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007,…
## $ pop                    <dbl> 20093756, 20966463, 21979923, 23064851, 2411897…
## $ incidence_100k         <dbl> 190, 189, 189, 189, 189, 189, 189, 189, 189, 18…
## $ incidence_number       <dbl> 38000, 40000, 42000, 44000, 46000, 47000, 49000…
## $ hiv_percent            <dbl> 0.36, 0.30, 0.26, 0.23, 0.22, 0.22, 0.22, 0.23,…
## $ hiv_incidence_100k     <dbl> 0.68, 0.57, 0.49, 0.44, 0.41, 0.42, 0.42, 0.43,…
## $ hiv_number             <dbl> 140, 120, 110, 100, 100, 100, 110, 120, 120, 13…
## $ mort_nohiv_100k        <dbl> 67.00, 62.00, 56.00, 57.00, 51.00, 46.00, 42.00…
## $ mort_nohiv_number      <dbl> 14000, 13000, 12000, 13000, 12000, 12000, 11000…
## $ mort_hiv_100k          <dbl> 0.15, 0.17, 0.27, 0.25, 0.21, 0.19, 0.18, 0.17,…
## $ mort_hiv_number        <dbl> 31, 35, 60, 57, 50, 48, 46, 45, 48, 55, 56, 61,…
## $ mort_100k              <dbl> 67.00, 62.00, 56.00, 57.00, 51.00, 46.00, 42.00…
## $ mort_number            <dbl> 14000, 13000, 12000, 13000, 12000, 12000, 11000…
## $ case_fatality_ratio    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ new_incidence_100k     <dbl> 35, 48, 63, 60, 76, 87, 98, 108, 104, 93, 97, 9…
## $ case_detection_percent <dbl> 19, 26, 33, 32, 40, 46, 52, 57, 55, 49, 51, 50,…
```

---

# Let's look at the `head()`

```r
head(tb)
```

```
## # A tibble: 6 × 18
##   country    who_region  year    pop incidence_100k incidence_number hiv_percent
##   <chr>      <chr>      <dbl>  <dbl>          <dbl>            <dbl>       <dbl>
## 1 Afghanist… EMR         2000 2.01e7            190            38000        0.36
## 2 Afghanist… EMR         2001 2.10e7            189            40000        0.3 
## 3 Afghanist… EMR         2002 2.20e7            189            42000        0.26
## 4 Afghanist… EMR         2003 2.31e7            189            44000        0.23
## 5 Afghanist… EMR         2004 2.41e7            189            46000        0.22
## 6 Afghanist… EMR         2005 2.51e7            189            47000        0.22
## # ℹ 11 more variables: hiv_incidence_100k <dbl>, hiv_number <dbl>,
## #   mort_nohiv_100k <dbl>, mort_nohiv_number <dbl>, mort_hiv_100k <dbl>,
## #   mort_hiv_number <dbl>, mort_100k <dbl>, mort_number <dbl>,
## #   case_fatality_ratio <dbl>, new_incidence_100k <dbl>,
## #   case_detection_percent <dbl>
```

---

# Let's look at the `names()`

```r
dim(tb)

names(tb)
```

```
## [1] 3850   18
```

```
##  [1] "country"                "who_region"             "year"                  
##  [4] "pop"                    "incidence_100k"         "incidence_number"      
##  [7] "hiv_percent"            "hiv_incidence_100k"     "hiv_number"            
## [10] "mort_nohiv_100k"        "mort_nohiv_number"      "mort_hiv_100k"         
## [13] "mort_hiv_number"        "mort_100k"              "mort_number"           
## [16] "case_fatality_ratio"    "new_incidence_100k"     "case_detection_percent"
```

---

## Let's look at the different countries

### Find the unique countries in the bottom 50 rows of tb.

```r
# without the pipe
unique(tail(tb, n = 50)$country)

# with the pipe
tb %>% 
  tail(50) %>%
  distinct(country)
```

```
## [1] "Yemen"    "Zambia"   "Zimbabwe"
```

```
## # A tibble: 3 × 1
##   country 
##   <chr>   
## 1 Yemen   
## 2 Zambia  
## 3 Zimbabwe
```

---

### Find the unique countries in the bottom 50 rows of `tb`

You will notice that we used different functions to complete our task.

The code without the pipe uses functions from base R while the code with the pipe uses a mixture (`tail()` from base R and `distinct()` from `dplyr`).

Not all functions work with the pipe, but we will usually opt for those that do when we have a choice.

---
class: middle

##  `distinct()` and `count()`

The `distinct()` function will return the distinct values of a column, while `count()` provides both the distinct values of a column and then number of times each value shows up.

```r
tb %>% 
  distinct(who_region) 
```

```
## # A tibble: 6 × 1
##   who_region
##   <chr>     
## 1 EMR       
## 2 EUR       
## 3 AFR       
## 4 WPR       
## 5 AMR       
## 6 SEA
```

]

```r
tb %>% 
  count(who_region)
```

```
## # A tibble: 6 × 2
##   who_region     n
##   <chr>      <int>
## 1 AFR          835
## 2 AMR          808
## 3 EMR          396
## 4 EUR          967
## 5 SEA          196
## 6 WPR          648
```

]

Notice that there is a new column produced by the count function called `n`.

---

class:middle

##  `arrange()`

The `arrange()` function does what it sounds like. It takes a data frame or tbl and arranges (or sorts) by column(s) of interest.

The first argument is the data, and subsequent arguments are columns to sort on.

Use the `desc()` function to arrange by descending.

```r
tb %>% 
  count(who_region) %>% 
  arrange(n)
```

```
## # A tibble: 6 × 2
##   who_region     n
##   <chr>      <int>
## 1 SEA          196
## 2 EMR          396
## 3 WPR          648
## 4 AMR          808
## 5 AFR          835
## 6 EUR          967
```

]

```r
tb %>% 
  count(who_region) %>% 
  arrange(desc(n)) # use can also use  arrange(-n)
```

```
## # A tibble: 6 × 2
##   who_region     n
##   <chr>      <int>
## 1 EUR          967
## 2 AFR          835
## 3 AMR          808
## 4 WPR          648
## 5 EMR          396
## 6 SEA          196
```

]

##  `filter()`

If you want to return **rows** of the data where some criteria are met, use the `filter()` function.

This is how we subset in the tidyverse. (Base R function is `subset()`)

![:scale 90%](data:image/png;base64,#dplyr_filter.png)

---

## Logical Operators

Here are the logical criteria in R:

- `==`: *Equal to*
- `! =`: *Not equal to*
- `>`: *Greater than*
- `> =`: *Greater than or equal to*
- `< `: *Less than*
- `< =`: *Less than or equal to*

<ul>
  <li>`==`: Equal to</li>
  <li>`! =`: Not Equal to</li>
  <li>`>`: Greater than</li>
</ul>

</div>

If you want to satisfy *all* of multiple conditions, you can use the "and" operator, `&`.

The "or" operator `|` (the vertical pipe character, shift-backslash) will return a subset that meet *any* of the conditions.

---

## Filter 2015 and above

```r
tb %>% 
  filter(year >= 2015) 
```

```
## # A tibble: 648 × 18
##    country   who_region  year    pop incidence_100k incidence_number hiv_percent
##    <chr>     <chr>      <dbl>  <dbl>          <dbl>            <dbl>       <dbl>
##  1 Afghanis… EMR         2015 3.37e7          189              64000        0.3 
##  2 Afghanis… EMR         2016 3.47e7          189              65000        0.31
##  3 Afghanis… EMR         2017 3.55e7          189              67000        0.31
##  4 Albania   EUR         2015 2.92e6           16                480        0.82
##  5 Albania   EUR         2016 2.93e6           16                480        0.83
##  6 Albania   EUR         2017 2.93e6           20                580        0.85
##  7 Algeria   AFR         2015 3.99e7           74              30000        0.58
##  8 Algeria   AFR         2016 4.06e7           70              29000        0.6 
##  9 Algeria   AFR         2017 4.13e7           70              29000        0.62
## 10 American… WPR         2015 5.55e4            8.3                5        0.1 
## # ℹ 638 more rows
## # ℹ 11 more variables: hiv_incidence_100k <dbl>, hiv_number <dbl>,
## #   mort_nohiv_100k <dbl>, mort_nohiv_number <dbl>, mort_hiv_100k <dbl>,
## #   mort_hiv_number <dbl>, mort_100k <dbl>, mort_number <dbl>,
## #   case_fatality_ratio <dbl>, new_incidence_100k <dbl>,
## #   case_detection_percent <dbl>
```

---

## Filter India

```r
tb %>% 
  filter(country == "India") 
```

```
## # A tibble: 18 × 18
##    country who_region  year      pop incidence_100k incidence_number hiv_percent
##    <chr>   <chr>      <dbl>    <dbl>          <dbl>            <dbl>       <dbl>
##  1 India   SEA         2000   1.05e9            289          3040000         7  
##  2 India   SEA         2001   1.07e9            288          3090000         7  
##  3 India   SEA         2002   1.09e9            287          3130000         7  
##  4 India   SEA         2003   1.11e9            285          3160000         7  
##  5 India   SEA         2004   1.13e9            282          3180000         7  
##  6 India   SEA         2005   1.14e9            279          3190000         7  
##  7 India   SEA         2006   1.16e9            274          3180000         7  
##  8 India   SEA         2007   1.18e9            268          3160000         7  
##  9 India   SEA         2008   1.20e9            261          3130000         6.8
## 10 India   SEA         2009   1.21e9            254          3090000         6.6
## 11 India   SEA         2010   1.23e9            247          3050000         6.3
## 12 India   SEA         2011   1.25e9            241          3000000         5.9
## 13 India   SEA         2012   1.26e9            234          2960000         5.4
## 14 India   SEA         2013   1.28e9            228          2920000         4.9
## 15 India   SEA         2014   1.29e9            223          2880000         4.3
## 16 India   SEA         2015   1.31e9            217          2840000         4  
## 17 India   SEA         2016   1.32e9            211          2790000         3.3
## 18 India   SEA         2017   1.34e9            204          2740000         3.1
## # ℹ 11 more variables: hiv_incidence_100k <dbl>, hiv_number <dbl>,
## #   mort_nohiv_100k <dbl>, mort_nohiv_number <dbl>, mort_hiv_100k <dbl>,
## #   mort_hiv_number <dbl>, mort_100k <dbl>, mort_number <dbl>,
## #   case_fatality_ratio <dbl>, new_incidence_100k <dbl>,
## #   case_detection_percent <dbl>
```

---

# Both India and 2015 or more recent

```r
tb %>% 
  filter(year >= 2015 & country == "India")
```

```
## # A tibble: 3 × 18
##   country who_region  year       pop incidence_100k incidence_number hiv_percent
##   <chr>   <chr>      <dbl>     <dbl>          <dbl>            <dbl>       <dbl>
## 1 India   SEA         2015    1.31e9            217          2840000         4  
## 2 India   SEA         2016    1.32e9            211          2790000         3.3
## 3 India   SEA         2017    1.34e9            204          2740000         3.1
## # ℹ 11 more variables: hiv_incidence_100k <dbl>, hiv_number <dbl>,
## #   mort_nohiv_100k <dbl>, mort_nohiv_number <dbl>, mort_hiv_100k <dbl>,
## #   mort_hiv_number <dbl>, mort_100k <dbl>, mort_number <dbl>,
## #   case_fatality_ratio <dbl>, new_incidence_100k <dbl>,
## #   case_detection_percent <dbl>
```

---

## `%in%` function

To `filter()` a categorical variable for only certain levels, we can use the `%in%` operator.

Let's see data from India, Nepal, Pakistan and Bangladesh First we will have to figure out how those are spelled in this dataset.

Open the spreadsheet viewer and find out.

We'll see a way to find them in code later on in the course.

---

## Indian Subcontinent

```r
indian_subcont <- c("India",
           "Nepal",
           "Pakistan",
           "Bangladesh")

tb %>% filter(country %in% indian_subcont)
```

```
## # A tibble: 72 × 18
##    country   who_region  year    pop incidence_100k incidence_number hiv_percent
##    <chr>     <chr>      <dbl>  <dbl>          <dbl>            <dbl>       <dbl>
##  1 Banglade… SEA         2000 1.32e8            221           291000        0.07
##  2 Banglade… SEA         2001 1.34e8            221           297000        0.07
##  3 Banglade… SEA         2002 1.37e8            221           302000        0.08
##  4 Banglade… SEA         2003 1.39e8            221           307000        0.09
##  5 Banglade… SEA         2004 1.41e8            221           313000        0.1 
##  6 Banglade… SEA         2005 1.43e8            221           317000        0.12
##  7 Banglade… SEA         2006 1.45e8            221           322000        0.13
##  8 Banglade… SEA         2007 1.47e8            221           325000        0.14
##  9 Banglade… SEA         2008 1.49e8            221           329000        0.14
## 10 Banglade… SEA         2009 1.50e8            221           333000        0.14
## # ℹ 62 more rows
## # ℹ 11 more variables: hiv_incidence_100k <dbl>, hiv_number <dbl>,
## #   mort_nohiv_100k <dbl>, mort_nohiv_number <dbl>, mort_hiv_100k <dbl>,
## #   mort_hiv_number <dbl>, mort_100k <dbl>, mort_number <dbl>,
## #   case_fatality_ratio <dbl>, new_incidence_100k <dbl>,
## #   case_detection_percent <dbl>
```

---

##  Other Useful Functions

## `drop_na()`

The `drop_na()` function is extremely useful for when we need to subset a variable to remove missing values.

## `select()`

Whereas the `filter()` function allows you to return only certain _rows_ matching a condition, the `select()` function returns only certain _columns_. The first argument is the data, and subsequent arguments are the columns you want.

---

## `summarize()`

The `summarize()` function summarizes multiple values to a single value.

On its own the `summarize()` function doesn't seem to be all that useful.

The dplyr package provides a few convenience functions called `n()` and `n_distinct()` that tell you the number of observations or the number of distinct values of a particular variable.

`summarize()` is the same as `summarise()`

```r
tb %>% 
  summarize(hiv_percent = mean(hiv_percent, na.rm = TRUE))
```

```
## # A tibble: 1 × 1
##   hiv_percent
##         <dbl>
## 1        12.1
```

---

## `group_by()`

We saw that `summarize()` isn't that useful on its own. Neither is `group_by()`.

All this does is takes an existing data frame and converts it into a grouped data frame where operations are performed by group.

```r
tb %>% 
  group_by(year)
```

```
## # A tibble: 3,850 × 18
## # Groups:   year [18]
##    country   who_region  year    pop incidence_100k incidence_number hiv_percent
##    <chr>     <chr>      <dbl>  <dbl>          <dbl>            <dbl>       <dbl>
##  1 Afghanis… EMR         2000 2.01e7            190            38000        0.36
##  2 Afghanis… EMR         2001 2.10e7            189            40000        0.3 
##  3 Afghanis… EMR         2002 2.20e7            189            42000        0.26
##  4 Afghanis… EMR         2003 2.31e7            189            44000        0.23
##  5 Afghanis… EMR         2004 2.41e7            189            46000        0.22
##  6 Afghanis… EMR         2005 2.51e7            189            47000        0.22
##  7 Afghanis… EMR         2006 2.59e7            189            49000        0.22
##  8 Afghanis… EMR         2007 2.66e7            189            50000        0.23
##  9 Afghanis… EMR         2008 2.73e7            189            52000        0.23
## 10 Afghanis… EMR         2009 2.80e7            189            53000        0.24
## # ℹ 3,840 more rows
## # ℹ 11 more variables: hiv_incidence_100k <dbl>, hiv_number <dbl>,
## #   mort_nohiv_100k <dbl>, mort_nohiv_number <dbl>, mort_hiv_100k <dbl>,
## #   mort_hiv_number <dbl>, mort_100k <dbl>, mort_number <dbl>,
## #   case_fatality_ratio <dbl>, new_incidence_100k <dbl>,
## #   case_detection_percent <dbl>
```

---

## Two variable `group_by()`

```r
tb %>% 
  group_by(year, who_region)
```

```
## # A tibble: 3,850 × 18
## # Groups:   year, who_region [108]
##    country   who_region  year    pop incidence_100k incidence_number hiv_percent
##    <chr>     <chr>      <dbl>  <dbl>          <dbl>            <dbl>       <dbl>
##  1 Afghanis… EMR         2000 2.01e7            190            38000        0.36
##  2 Afghanis… EMR         2001 2.10e7            189            40000        0.3 
##  3 Afghanis… EMR         2002 2.20e7            189            42000        0.26
##  4 Afghanis… EMR         2003 2.31e7            189            44000        0.23
##  5 Afghanis… EMR         2004 2.41e7            189            46000        0.22
##  6 Afghanis… EMR         2005 2.51e7            189            47000        0.22
##  7 Afghanis… EMR         2006 2.59e7            189            49000        0.22
##  8 Afghanis… EMR         2007 2.66e7            189            50000        0.23
##  9 Afghanis… EMR         2008 2.73e7            189            52000        0.23
## 10 Afghanis… EMR         2009 2.80e7            189            53000        0.24
## # ℹ 3,840 more rows
## # ℹ 11 more variables: hiv_incidence_100k <dbl>, hiv_number <dbl>,
## #   mort_nohiv_100k <dbl>, mort_nohiv_number <dbl>, mort_hiv_100k <dbl>,
## #   mort_hiv_number <dbl>, mort_100k <dbl>, mort_number <dbl>,
## #   case_fatality_ratio <dbl>, new_incidence_100k <dbl>,
## #   case_detection_percent <dbl>
```

---

## `group_by()` and `summarize()` together

The real power comes in where `group_by()` and `summarize()` are used together. First, write the `group_by()` statement. Then pipe the result to a call to `summarize()`.

```r
tb %>% 
  group_by(year) %>% 
  summarize(mean_inc = mean(incidence_100k, na.rm = TRUE))
```

```
## # A tibble: 18 × 2
##     year mean_inc
##    <dbl>    <dbl>
##  1  2000     138.
##  2  2001     138.
##  3  2002     140.
##  4  2003     141.
##  5  2004     139.
##  6  2005     137.
##  7  2006     135.
##  8  2007     132.
##  9  2008     129.
## 10  2009     127.
## 11  2010     124.
## 12  2011     120.
## 13  2012     116.
## 14  2013     113.
## 15  2014     109.
## 16  2015     107.
## 17  2016     104.
## 18  2017     101.
```

---

## `mutate()`

Mutate creates a new variable or modifies an existing one.

![:scale 90%](data:image/png;base64,#dplyr_mutate.png)

---

## `ifelse()`

Lets create a column called `ind_sub` if the country is in the Indian Subcontinent.

```r
tb %>% 
  mutate(indian_sub1 = ifelse(country %in% indian_subcont, 
                              "Indian Subcontinent", "Others"))
```

```
## # A tibble: 3,850 × 19
##    country   who_region  year    pop incidence_100k incidence_number hiv_percent
##    <chr>     <chr>      <dbl>  <dbl>          <dbl>            <dbl>       <dbl>
##  1 Afghanis… EMR         2000 2.01e7            190            38000        0.36
##  2 Afghanis… EMR         2001 2.10e7            189            40000        0.3 
##  3 Afghanis… EMR         2002 2.20e7            189            42000        0.26
##  4 Afghanis… EMR         2003 2.31e7            189            44000        0.23
##  5 Afghanis… EMR         2004 2.41e7            189            46000        0.22
##  6 Afghanis… EMR         2005 2.51e7            189            47000        0.22
##  7 Afghanis… EMR         2006 2.59e7            189            49000        0.22
##  8 Afghanis… EMR         2007 2.66e7            189            50000        0.23
##  9 Afghanis… EMR         2008 2.73e7            189            52000        0.23
## 10 Afghanis… EMR         2009 2.80e7            189            53000        0.24
## # ℹ 3,840 more rows
## # ℹ 12 more variables: hiv_incidence_100k <dbl>, hiv_number <dbl>,
## #   mort_nohiv_100k <dbl>, mort_nohiv_number <dbl>, mort_hiv_100k <dbl>,
## #   mort_hiv_number <dbl>, mort_100k <dbl>, mort_number <dbl>,
## #   case_fatality_ratio <dbl>, new_incidence_100k <dbl>,
## #   case_detection_percent <dbl>, indian_sub1 <chr>
```

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## `case_when()`

Alternative of `ifelse()`

![:scale 90%](data:image/png;base64,#case_when.jpg)

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class: middle

# `ifelse()` vs `case_when()`

Note that the `if_else()` function may result in slightly shorter code if you only need to code for 2 options.

For more options, nested `if_else()` statements become hard to read and could result in mismatched parentheses so `case_when()` will be a more elegant solution.

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# More Resources for `dplyr`

- Check out the [Data Wrangling Cheatsheet](https://rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf) that covers dplyr and tidyr functions.

- Review the [Tibbles](https://r4ds.had.co.nz/tibbles.html) chapter of the excellent, free [**_R for Data Science_ book**](http://r4ds.had.co.nz).

- Check out the [Transformations](https://r4ds.had.co.nz/transform.html) chapter to learn more about the `dplyr` package. Note that this chapter also uses the graphing package `ggplot2` which we have covered yesterday.

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# `skimr` Package

`skimr` is designed to provide summary statistics about variables in data frames, tibbles, data tables and vectors.

The core function of `skimr` is the `skim()` function, which is designed to work with (grouped) data frames, and will try coerce other objects to data frames if possible.

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# `DataExplorer` Package

The `DataExplorer` package aims to automate most of data handling and visualization, so that users could focus on studying the data and extracting insights.^[[DataExplorer Package](https://boxuancui.github.io/DataExplorer/)]

The single most important function from the `DataExplorer` package is `create_report()`

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# Lets Begin!